Overview

Dataset statistics

Number of variables23
Number of observations145460
Missing cells343248
Missing cells (%)10.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.5 MiB
Average record size in memory184.0 B

Variable types

Categorical5
Numeric16
Boolean2

Alerts

Date has a high cardinality: 3436 distinct valuesHigh cardinality
MinTemp is highly overall correlated with Location and 5 other fieldsHigh correlation
MaxTemp is highly overall correlated with Location and 5 other fieldsHigh correlation
Evaporation is highly overall correlated with MaxTempHigh correlation
Sunshine is highly overall correlated with Humidity9am and 4 other fieldsHigh correlation
WindGustSpeed is highly overall correlated with WindSpeed9am and 1 other fieldsHigh correlation
WindSpeed9am is highly overall correlated with WindGustSpeed and 1 other fieldsHigh correlation
WindSpeed3pm is highly overall correlated with WindGustSpeed and 1 other fieldsHigh correlation
Humidity9am is highly overall correlated with Location and 6 other fieldsHigh correlation
Humidity3pm is highly overall correlated with Location and 8 other fieldsHigh correlation
Pressure9am is highly overall correlated with MinTemp and 2 other fieldsHigh correlation
Pressure3pm is highly overall correlated with MinTemp and 2 other fieldsHigh correlation
Cloud9am is highly overall correlated with Sunshine and 3 other fieldsHigh correlation
Cloud3pm is highly overall correlated with Sunshine and 3 other fieldsHigh correlation
Temp9am is highly overall correlated with Location and 6 other fieldsHigh correlation
Temp3pm is highly overall correlated with Location and 5 other fieldsHigh correlation
Location is highly overall correlated with MinTemp and 8 other fieldsHigh correlation
WindGustDir is highly overall correlated with Location and 2 other fieldsHigh correlation
WindDir9am is highly overall correlated with Location and 2 other fieldsHigh correlation
WindDir3pm is highly overall correlated with Location and 2 other fieldsHigh correlation
RainToday is highly overall correlated with Humidity3pmHigh correlation
RainTomorrow is highly overall correlated with Sunshine and 2 other fieldsHigh correlation
MinTemp has 1485 (1.0%) missing valuesMissing
Rainfall has 3261 (2.2%) missing valuesMissing
Evaporation has 62790 (43.2%) missing valuesMissing
Sunshine has 69835 (48.0%) missing valuesMissing
WindGustDir has 10326 (7.1%) missing valuesMissing
WindGustSpeed has 10263 (7.1%) missing valuesMissing
WindDir9am has 10566 (7.3%) missing valuesMissing
WindDir3pm has 4228 (2.9%) missing valuesMissing
WindSpeed9am has 1767 (1.2%) missing valuesMissing
WindSpeed3pm has 3062 (2.1%) missing valuesMissing
Humidity9am has 2654 (1.8%) missing valuesMissing
Humidity3pm has 4507 (3.1%) missing valuesMissing
Pressure9am has 15065 (10.4%) missing valuesMissing
Pressure3pm has 15028 (10.3%) missing valuesMissing
Cloud9am has 55888 (38.4%) missing valuesMissing
Cloud3pm has 59358 (40.8%) missing valuesMissing
Temp9am has 1767 (1.2%) missing valuesMissing
Temp3pm has 3609 (2.5%) missing valuesMissing
RainToday has 3261 (2.2%) missing valuesMissing
RainTomorrow has 3267 (2.2%) missing valuesMissing
Rainfall has 91080 (62.6%) zerosZeros
Sunshine has 2359 (1.6%) zerosZeros
WindSpeed9am has 8745 (6.0%) zerosZeros
Cloud9am has 8642 (5.9%) zerosZeros
Cloud3pm has 4974 (3.4%) zerosZeros

Reproduction

Analysis started2023-05-03 03:11:38.106396
Analysis finished2023-05-03 03:12:24.557381
Duration46.45 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Date
Categorical

Distinct3436
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2013-11-12
 
49
2014-09-01
 
49
2014-08-23
 
49
2014-08-24
 
49
2014-08-25
 
49
Other values (3431)
145215 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1454600
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)0.1%

Sample

1st row2008-12-01
2nd row2008-12-02
3rd row2008-12-03
4th row2008-12-04
5th row2008-12-05

Common Values

ValueCountFrequency (%)
2013-11-12 49
 
< 0.1%
2014-09-01 49
 
< 0.1%
2014-08-23 49
 
< 0.1%
2014-08-24 49
 
< 0.1%
2014-08-25 49
 
< 0.1%
2014-08-26 49
 
< 0.1%
2014-08-27 49
 
< 0.1%
2014-08-28 49
 
< 0.1%
2014-08-29 49
 
< 0.1%
2014-08-30 49
 
< 0.1%
Other values (3426) 144970
99.7%

Length

2023-05-02T23:12:24.631850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013-11-12 49
 
< 0.1%
2016-12-13 49
 
< 0.1%
2017-01-03 49
 
< 0.1%
2017-01-02 49
 
< 0.1%
2017-01-01 49
 
< 0.1%
2016-12-31 49
 
< 0.1%
2016-12-30 49
 
< 0.1%
2016-12-29 49
 
< 0.1%
2016-12-28 49
 
< 0.1%
2016-12-27 49
 
< 0.1%
Other values (3426) 144970
99.7%

Most occurring characters

ValueCountFrequency (%)
0 361057
24.8%
- 290920
20.0%
1 266467
18.3%
2 244481
16.8%
3 51292
 
3.5%
5 45594
 
3.1%
6 44926
 
3.1%
4 43792
 
3.0%
9 42450
 
2.9%
7 35016
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1163680
80.0%
Dash Punctuation 290920
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 361057
31.0%
1 266467
22.9%
2 244481
21.0%
3 51292
 
4.4%
5 45594
 
3.9%
6 44926
 
3.9%
4 43792
 
3.8%
9 42450
 
3.6%
7 35016
 
3.0%
8 28605
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 290920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1454600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 361057
24.8%
- 290920
20.0%
1 266467
18.3%
2 244481
16.8%
3 51292
 
3.5%
5 45594
 
3.1%
6 44926
 
3.1%
4 43792
 
3.0%
9 42450
 
2.9%
7 35016
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1454600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 361057
24.8%
- 290920
20.0%
1 266467
18.3%
2 244481
16.8%
3 51292
 
3.5%
5 45594
 
3.1%
6 44926
 
3.1%
4 43792
 
3.0%
9 42450
 
2.9%
7 35016
 
2.4%

Location
Categorical

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Canberra
 
3436
Sydney
 
3344
Darwin
 
3193
Melbourne
 
3193
Brisbane
 
3193
Other values (44)
129101 

Length

Max length16
Median length11
Mean length8.7116252
Min length4

Characters and Unicode

Total characters1267193
Distinct characters40
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlbury
2nd rowAlbury
3rd rowAlbury
4th rowAlbury
5th rowAlbury

Common Values

ValueCountFrequency (%)
Canberra 3436
 
2.4%
Sydney 3344
 
2.3%
Darwin 3193
 
2.2%
Melbourne 3193
 
2.2%
Brisbane 3193
 
2.2%
Adelaide 3193
 
2.2%
Perth 3193
 
2.2%
Hobart 3193
 
2.2%
Albany 3040
 
2.1%
MountGambier 3040
 
2.1%
Other values (39) 113442
78.0%

Length

2023-05-02T23:12:24.725243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
canberra 3436
 
2.4%
sydney 3344
 
2.3%
darwin 3193
 
2.2%
melbourne 3193
 
2.2%
brisbane 3193
 
2.2%
adelaide 3193
 
2.2%
perth 3193
 
2.2%
hobart 3193
 
2.2%
launceston 3040
 
2.1%
wollongong 3040
 
2.1%
Other values (39) 113442
78.0%

Most occurring characters

ValueCountFrequency (%)
a 117797
 
9.3%
r 116473
 
9.2%
o 109016
 
8.6%
e 104586
 
8.3%
n 90638
 
7.2%
l 79075
 
6.2%
i 76233
 
6.0%
t 59332
 
4.7%
d 36868
 
2.9%
u 36585
 
2.9%
Other values (30) 440590
34.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1070469
84.5%
Uppercase Letter 196724
 
15.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 117797
11.0%
r 116473
10.9%
o 109016
10.2%
e 104586
9.8%
n 90638
 
8.5%
l 79075
 
7.4%
i 76233
 
7.1%
t 59332
 
5.5%
d 36868
 
3.4%
u 36585
 
3.4%
Other values (12) 243866
22.8%
Uppercase Letter
ValueCountFrequency (%)
A 27358
13.9%
W 24100
12.3%
C 18543
9.4%
M 18300
9.3%
S 15403
7.8%
P 15259
7.8%
N 13639
6.9%
B 12282
6.2%
G 12121
 
6.2%
H 9206
 
4.7%
Other values (8) 30513
15.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1267193
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 117797
 
9.3%
r 116473
 
9.2%
o 109016
 
8.6%
e 104586
 
8.3%
n 90638
 
7.2%
l 79075
 
6.2%
i 76233
 
6.0%
t 59332
 
4.7%
d 36868
 
2.9%
u 36585
 
2.9%
Other values (30) 440590
34.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1267193
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 117797
 
9.3%
r 116473
 
9.2%
o 109016
 
8.6%
e 104586
 
8.3%
n 90638
 
7.2%
l 79075
 
6.2%
i 76233
 
6.0%
t 59332
 
4.7%
d 36868
 
2.9%
u 36585
 
2.9%
Other values (30) 440590
34.8%

MinTemp
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct389
Distinct (%)0.3%
Missing1485
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean12.194034
Minimum-8.5
Maximum33.9
Zeros159
Zeros (%)0.1%
Negative3464
Negative (%)2.4%
Memory size1.1 MiB
2023-05-02T23:12:24.820430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-8.5
5-th percentile1.8
Q17.6
median12
Q316.9
95-th percentile23
Maximum33.9
Range42.4
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation6.398495
Coefficient of variation (CV)0.52472338
Kurtosis-0.48397212
Mean12.194034
Median Absolute Deviation (MAD)4.6
Skewness0.021188284
Sum1755636.1
Variance40.940738
MonotonicityNot monotonic
2023-05-02T23:12:24.917775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 899
 
0.6%
10.2 898
 
0.6%
9.6 896
 
0.6%
10.5 884
 
0.6%
10.8 872
 
0.6%
9 872
 
0.6%
10 871
 
0.6%
12 866
 
0.6%
8.9 861
 
0.6%
10.4 860
 
0.6%
Other values (379) 135196
92.9%
(Missing) 1485
 
1.0%
ValueCountFrequency (%)
-8.5 1
 
< 0.1%
-8.2 2
 
< 0.1%
-8 2
 
< 0.1%
-7.8 1
 
< 0.1%
-7.6 2
 
< 0.1%
-7.5 2
 
< 0.1%
-7.3 1
 
< 0.1%
-7.2 1
 
< 0.1%
-7.1 1
 
< 0.1%
-7 7
< 0.1%
ValueCountFrequency (%)
33.9 1
 
< 0.1%
31.9 1
 
< 0.1%
31.8 1
 
< 0.1%
31.4 3
< 0.1%
31.2 1
 
< 0.1%
31 1
 
< 0.1%
30.7 2
< 0.1%
30.5 1
 
< 0.1%
30.3 1
 
< 0.1%
30.2 1
 
< 0.1%

MaxTemp
Real number (ℝ)

Distinct505
Distinct (%)0.4%
Missing1261
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean23.221348
Minimum-4.8
Maximum48.1
Zeros14
Zeros (%)< 0.1%
Negative113
Negative (%)0.1%
Memory size1.1 MiB
2023-05-02T23:12:25.023381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-4.8
5-th percentile12.8
Q117.9
median22.6
Q328.2
95-th percentile35.5
Maximum48.1
Range52.9
Interquartile range (IQR)10.3

Descriptive statistics

Standard deviation7.1190488
Coefficient of variation (CV)0.30657345
Kurtosis-0.22462978
Mean23.221348
Median Absolute Deviation (MAD)5.1
Skewness0.22083935
Sum3348495.2
Variance50.680856
MonotonicityNot monotonic
2023-05-02T23:12:25.131718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 885
 
0.6%
19 843
 
0.6%
19.8 840
 
0.6%
20.4 834
 
0.6%
19.9 823
 
0.6%
20.8 817
 
0.6%
19.5 812
 
0.6%
18.5 811
 
0.6%
21 810
 
0.6%
18.2 804
 
0.6%
Other values (495) 135920
93.4%
(Missing) 1261
 
0.9%
ValueCountFrequency (%)
-4.8 1
< 0.1%
-4.1 1
< 0.1%
-3.8 1
< 0.1%
-3.7 1
< 0.1%
-3.2 1
< 0.1%
-3.1 2
< 0.1%
-3 1
< 0.1%
-2.9 1
< 0.1%
-2.7 1
< 0.1%
-2.5 2
< 0.1%
ValueCountFrequency (%)
48.1 1
 
< 0.1%
47.3 2
< 0.1%
47 1
 
< 0.1%
46.9 1
 
< 0.1%
46.8 3
< 0.1%
46.7 2
< 0.1%
46.6 1
 
< 0.1%
46.5 1
 
< 0.1%
46.4 4
< 0.1%
46.3 2
< 0.1%

Rainfall
Real number (ℝ)

MISSING
ZEROS

Distinct681
Distinct (%)0.5%
Missing3261
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean2.3609181
Minimum0
Maximum371
Zeros91080
Zeros (%)62.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-05-02T23:12:25.232083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.8
95-th percentile13
Maximum371
Range371
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation8.4780597
Coefficient of variation (CV)3.5910011
Kurtosis178.15208
Mean2.3609181
Median Absolute Deviation (MAD)0
Skewness9.8362253
Sum335720.2
Variance71.877497
MonotonicityNot monotonic
2023-05-02T23:12:25.328806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 91080
62.6%
0.2 8761
 
6.0%
0.4 3782
 
2.6%
0.6 2592
 
1.8%
0.8 2056
 
1.4%
1 1759
 
1.2%
1.2 1535
 
1.1%
1.4 1377
 
0.9%
1.6 1200
 
0.8%
1.8 1104
 
0.8%
Other values (671) 26953
 
18.5%
(Missing) 3261
 
2.2%
ValueCountFrequency (%)
0 91080
62.6%
0.1 157
 
0.1%
0.2 8761
 
6.0%
0.3 65
 
< 0.1%
0.4 3782
 
2.6%
0.5 39
 
< 0.1%
0.6 2592
 
1.8%
0.7 13
 
< 0.1%
0.8 2056
 
1.4%
0.9 15
 
< 0.1%
ValueCountFrequency (%)
371 1
< 0.1%
367.6 1
< 0.1%
278.4 1
< 0.1%
268.6 1
< 0.1%
247.2 1
< 0.1%
240 1
< 0.1%
236.8 1
< 0.1%
225 1
< 0.1%
219.6 1
< 0.1%
216.3 1
< 0.1%

Evaporation
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct358
Distinct (%)0.4%
Missing62790
Missing (%)43.2%
Infinite0
Infinite (%)0.0%
Mean5.4682315
Minimum0
Maximum145
Zeros244
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-05-02T23:12:25.432626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.6
median4.8
Q37.4
95-th percentile12
Maximum145
Range145
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation4.1937041
Coefficient of variation (CV)0.76692146
Kurtosis45.043266
Mean5.4682315
Median Absolute Deviation (MAD)2.4
Skewness3.761286
Sum452058.7
Variance17.587154
MonotonicityNot monotonic
2023-05-02T23:12:25.528100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 3339
 
2.3%
8 2609
 
1.8%
2.2 2095
 
1.4%
2 2032
 
1.4%
2.6 2003
 
1.4%
2.4 2003
 
1.4%
1.8 1979
 
1.4%
3 1973
 
1.4%
3.4 1967
 
1.4%
3.2 1956
 
1.3%
Other values (348) 60714
41.7%
(Missing) 62790
43.2%
ValueCountFrequency (%)
0 244
 
0.2%
0.1 8
 
< 0.1%
0.2 503
 
0.3%
0.3 10
 
< 0.1%
0.4 769
0.5%
0.5 14
 
< 0.1%
0.6 1097
0.8%
0.7 24
 
< 0.1%
0.8 1384
1.0%
0.9 28
 
< 0.1%
ValueCountFrequency (%)
145 1
< 0.1%
86.2 1
< 0.1%
82.4 1
< 0.1%
81.2 1
< 0.1%
77.3 1
< 0.1%
74.8 1
< 0.1%
72.2 1
< 0.1%
70.4 1
< 0.1%
70 1
< 0.1%
68.8 2
< 0.1%

Sunshine
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct145
Distinct (%)0.2%
Missing69835
Missing (%)48.0%
Infinite0
Infinite (%)0.0%
Mean7.6111775
Minimum0
Maximum14.5
Zeros2359
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-05-02T23:12:25.632379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q14.8
median8.4
Q310.6
95-th percentile12.8
Maximum14.5
Range14.5
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation3.785483
Coefficient of variation (CV)0.49735839
Kurtosis-0.82945934
Mean7.6111775
Median Absolute Deviation (MAD)2.6
Skewness-0.49648004
Sum575595.3
Variance14.329881
MonotonicityNot monotonic
2023-05-02T23:12:25.742707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2359
 
1.6%
10.7 1101
 
0.8%
11 1094
 
0.8%
10.8 1069
 
0.7%
10.5 1027
 
0.7%
10.9 1021
 
0.7%
10.3 1010
 
0.7%
10.2 993
 
0.7%
10 984
 
0.7%
11.1 978
 
0.7%
Other values (135) 63989
44.0%
(Missing) 69835
48.0%
ValueCountFrequency (%)
0 2359
1.6%
0.1 542
 
0.4%
0.2 521
 
0.4%
0.3 433
 
0.3%
0.4 326
 
0.2%
0.5 322
 
0.2%
0.6 298
 
0.2%
0.7 344
 
0.2%
0.8 320
 
0.2%
0.9 323
 
0.2%
ValueCountFrequency (%)
14.5 1
 
< 0.1%
14.3 4
 
< 0.1%
14.2 2
 
< 0.1%
14.1 6
 
< 0.1%
14 15
 
< 0.1%
13.9 22
 
< 0.1%
13.8 60
 
< 0.1%
13.7 118
0.1%
13.6 181
0.1%
13.5 183
0.1%

WindGustDir
Categorical

HIGH CORRELATION
MISSING

Distinct16
Distinct (%)< 0.1%
Missing10326
Missing (%)7.1%
Memory size1.1 MiB
W
9915 
SE
9418 
N
9313 
SSE
9216 
E
9181 
Other values (11)
88091 

Length

Max length3
Median length2
Mean length2.1949176
Min length1

Characters and Unicode

Total characters296608
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowWNW
3rd rowWSW
4th rowNE
5th rowW

Common Values

ValueCountFrequency (%)
W 9915
 
6.8%
SE 9418
 
6.5%
N 9313
 
6.4%
SSE 9216
 
6.3%
E 9181
 
6.3%
S 9168
 
6.3%
WSW 9069
 
6.2%
SW 8967
 
6.2%
SSW 8736
 
6.0%
WNW 8252
 
5.7%
Other values (6) 43899
30.2%
(Missing) 10326
 
7.1%

Length

2023-05-02T23:12:25.844630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
w 9915
 
7.3%
se 9418
 
7.0%
n 9313
 
6.9%
sse 9216
 
6.8%
e 9181
 
6.8%
s 9168
 
6.8%
wsw 9069
 
6.7%
sw 8967
 
6.6%
ssw 8736
 
6.5%
wnw 8252
 
6.1%
Other values (6) 43899
32.5%

Most occurring characters

ValueCountFrequency (%)
S 79898
26.9%
W 77002
26.0%
E 72448
24.4%
N 67260
22.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 296608
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 79898
26.9%
W 77002
26.0%
E 72448
24.4%
N 67260
22.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 296608
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 79898
26.9%
W 77002
26.0%
E 72448
24.4%
N 67260
22.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 296608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 79898
26.9%
W 77002
26.0%
E 72448
24.4%
N 67260
22.7%

WindGustSpeed
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct67
Distinct (%)< 0.1%
Missing10263
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean40.03523
Minimum6
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-05-02T23:12:25.940775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile20
Q131
median39
Q348
95-th percentile65
Maximum135
Range129
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.607062
Coefficient of variation (CV)0.33987721
Kurtosis1.4186423
Mean40.03523
Median Absolute Deviation (MAD)9
Skewness0.87487888
Sum5412643
Variance185.15214
MonotonicityNot monotonic
2023-05-02T23:12:26.062246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 9215
 
6.3%
39 8794
 
6.0%
31 8428
 
5.8%
37 8047
 
5.5%
33 7933
 
5.5%
41 7369
 
5.1%
30 7038
 
4.8%
43 6609
 
4.5%
28 6478
 
4.5%
44 5432
 
3.7%
Other values (57) 59854
41.1%
(Missing) 10263
 
7.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
7 19
 
< 0.1%
9 91
 
0.1%
11 192
 
0.1%
13 532
 
0.4%
15 835
 
0.6%
17 1387
1.0%
19 1751
1.2%
20 2627
1.8%
22 2810
1.9%
ValueCountFrequency (%)
135 3
 
< 0.1%
130 1
 
< 0.1%
126 2
 
< 0.1%
124 2
 
< 0.1%
122 3
 
< 0.1%
120 4
< 0.1%
117 4
< 0.1%
115 5
< 0.1%
113 8
< 0.1%
111 3
 
< 0.1%

WindDir9am
Categorical

HIGH CORRELATION
MISSING

Distinct16
Distinct (%)< 0.1%
Missing10566
Missing (%)7.3%
Memory size1.1 MiB
N
11758 
SE
9287 
E
9176 
SSE
9112 
NW
8749 
Other values (11)
86812 

Length

Max length3
Median length2
Mean length2.1828102
Min length1

Characters and Unicode

Total characters294448
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowNNW
3rd rowW
4th rowSE
5th rowENE

Common Values

ValueCountFrequency (%)
N 11758
 
8.1%
SE 9287
 
6.4%
E 9176
 
6.3%
SSE 9112
 
6.3%
NW 8749
 
6.0%
S 8659
 
6.0%
W 8459
 
5.8%
SW 8423
 
5.8%
NNE 8129
 
5.6%
NNW 7980
 
5.5%
Other values (6) 45162
31.0%
(Missing) 10566
 
7.3%

Length

2023-05-02T23:12:26.181300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n 11758
 
8.7%
se 9287
 
6.9%
e 9176
 
6.8%
sse 9112
 
6.8%
nw 8749
 
6.5%
s 8659
 
6.4%
w 8459
 
6.3%
sw 8423
 
6.2%
nne 8129
 
6.0%
nnw 7980
 
5.9%
Other values (6) 45162
33.5%

Most occurring characters

ValueCountFrequency (%)
N 75646
25.7%
S 74421
25.3%
E 74307
25.2%
W 70074
23.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 294448
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 75646
25.7%
S 74421
25.3%
E 74307
25.2%
W 70074
23.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 294448
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 75646
25.7%
S 74421
25.3%
E 74307
25.2%
W 70074
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 294448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 75646
25.7%
S 74421
25.3%
E 74307
25.2%
W 70074
23.8%

WindDir3pm
Categorical

HIGH CORRELATION
MISSING

Distinct16
Distinct (%)< 0.1%
Missing4228
Missing (%)2.9%
Memory size1.1 MiB
SE
10838 
W
10110 
S
9926 
WSW
9518 
SSE
9399 
Other values (11)
91441 

Length

Max length3
Median length2
Mean length2.2079628
Min length1

Characters and Unicode

Total characters311835
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWNW
2nd rowWSW
3rd rowWSW
4th rowE
5th rowNW

Common Values

ValueCountFrequency (%)
SE 10838
 
7.5%
W 10110
 
7.0%
S 9926
 
6.8%
WSW 9518
 
6.5%
SSE 9399
 
6.5%
SW 9354
 
6.4%
N 8890
 
6.1%
WNW 8874
 
6.1%
NW 8610
 
5.9%
ESE 8505
 
5.8%
Other values (6) 47208
32.5%

Length

2023-05-02T23:12:26.281959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se 10838
 
7.7%
w 10110
 
7.2%
s 9926
 
7.0%
wsw 9518
 
6.7%
sse 9399
 
6.7%
sw 9354
 
6.6%
n 8890
 
6.3%
wnw 8874
 
6.3%
nw 8610
 
6.1%
ese 8505
 
6.0%
Other values (6) 47208
33.4%

Most occurring characters

ValueCountFrequency (%)
S 83251
26.7%
W 80884
25.9%
E 76286
24.5%
N 71414
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 311835
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 83251
26.7%
W 80884
25.9%
E 76286
24.5%
N 71414
22.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 311835
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 83251
26.7%
W 80884
25.9%
E 76286
24.5%
N 71414
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 311835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 83251
26.7%
W 80884
25.9%
E 76286
24.5%
N 71414
22.9%

WindSpeed9am
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct43
Distinct (%)< 0.1%
Missing1767
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean14.043426
Minimum0
Maximum130
Zeros8745
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-05-02T23:12:26.370553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median13
Q319
95-th percentile30
Maximum130
Range130
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.9153753
Coefficient of variation (CV)0.63484333
Kurtosis1.2269909
Mean14.043426
Median Absolute Deviation (MAD)6
Skewness0.77762951
Sum2017942
Variance79.483917
MonotonicityNot monotonic
2023-05-02T23:12:26.818087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
9 13649
 
9.4%
13 13132
 
9.0%
11 11728
 
8.1%
17 10788
 
7.4%
7 10783
 
7.4%
15 10625
 
7.3%
6 9118
 
6.3%
19 8763
 
6.0%
0 8745
 
6.0%
20 8063
 
5.5%
Other values (33) 38299
26.3%
ValueCountFrequency (%)
0 8745
6.0%
2 4609
 
3.2%
4 6360
4.4%
6 9118
6.3%
7 10783
7.4%
9 13649
9.4%
11 11728
8.1%
13 13132
9.0%
15 10625
7.3%
17 10788
7.4%
ValueCountFrequency (%)
130 1
 
< 0.1%
87 2
 
< 0.1%
83 1
 
< 0.1%
74 4
 
< 0.1%
72 1
 
< 0.1%
69 2
 
< 0.1%
67 4
 
< 0.1%
65 8
< 0.1%
63 9
< 0.1%
61 11
< 0.1%

WindSpeed3pm
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct44
Distinct (%)< 0.1%
Missing3062
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean18.662657
Minimum0
Maximum87
Zeros1112
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-05-02T23:12:26.917231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q113
median19
Q324
95-th percentile35
Maximum87
Range87
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.8098
Coefficient of variation (CV)0.47205498
Kurtosis0.76385824
Mean18.662657
Median Absolute Deviation (MAD)6
Skewness0.62821542
Sum2657525
Variance77.612576
MonotonicityNot monotonic
2023-05-02T23:12:27.023612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
13 12580
 
8.6%
17 12539
 
8.6%
20 11713
 
8.1%
15 11483
 
7.9%
19 11263
 
7.7%
11 10015
 
6.9%
9 9753
 
6.7%
24 9052
 
6.2%
22 8598
 
5.9%
28 6553
 
4.5%
Other values (34) 38849
26.7%
ValueCountFrequency (%)
0 1112
 
0.8%
2 1034
 
0.7%
4 2249
 
1.5%
6 3805
 
2.6%
7 5903
4.1%
9 9753
6.7%
11 10015
6.9%
13 12580
8.6%
15 11483
7.9%
17 12539
8.6%
ValueCountFrequency (%)
87 1
 
< 0.1%
83 2
 
< 0.1%
78 1
 
< 0.1%
76 2
 
< 0.1%
74 1
 
< 0.1%
72 2
 
< 0.1%
69 3
 
< 0.1%
67 1
 
< 0.1%
65 18
< 0.1%
63 13
< 0.1%

Humidity9am
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct101
Distinct (%)0.1%
Missing2654
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean68.880831
Minimum0
Maximum100
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-05-02T23:12:27.142638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q157
median70
Q383
95-th percentile98
Maximum100
Range100
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.029164
Coefficient of variation (CV)0.27626212
Kurtosis-0.037555042
Mean68.880831
Median Absolute Deviation (MAD)13
Skewness-0.48396899
Sum9836596
Variance362.1091
MonotonicityNot monotonic
2023-05-02T23:12:27.259517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 3391
 
2.3%
70 3026
 
2.1%
69 3023
 
2.1%
65 3014
 
2.1%
68 3011
 
2.1%
71 2976
 
2.0%
66 2973
 
2.0%
67 2950
 
2.0%
74 2917
 
2.0%
72 2914
 
2.0%
Other values (91) 112611
77.4%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 5
 
< 0.1%
2 8
 
< 0.1%
3 10
 
< 0.1%
4 20
 
< 0.1%
5 27
 
< 0.1%
6 37
< 0.1%
7 43
< 0.1%
8 56
< 0.1%
9 71
< 0.1%
ValueCountFrequency (%)
100 2863
2.0%
99 3391
2.3%
98 2099
1.4%
97 1789
1.2%
96 1609
1.1%
95 1636
1.1%
94 1764
1.2%
93 1862
1.3%
92 1755
1.2%
91 1869
1.3%

Humidity3pm
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct101
Distinct (%)0.1%
Missing4507
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean51.539116
Minimum0
Maximum100
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-05-02T23:12:27.362269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q137
median52
Q366
95-th percentile88
Maximum100
Range100
Interquartile range (IQR)29

Descriptive statistics

Standard deviation20.795902
Coefficient of variation (CV)0.40349745
Kurtosis-0.51136325
Mean51.539116
Median Absolute Deviation (MAD)14
Skewness0.033614368
Sum7264593
Variance432.46953
MonotonicityNot monotonic
2023-05-02T23:12:27.460337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 2751
 
1.9%
55 2738
 
1.9%
57 2728
 
1.9%
53 2697
 
1.9%
59 2690
 
1.8%
58 2643
 
1.8%
54 2642
 
1.8%
50 2624
 
1.8%
51 2621
 
1.8%
60 2615
 
1.8%
Other values (91) 114204
78.5%
(Missing) 4507
 
3.1%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 26
 
< 0.1%
2 35
 
< 0.1%
3 63
 
< 0.1%
4 113
 
0.1%
5 157
 
0.1%
6 242
0.2%
7 303
0.2%
8 422
0.3%
9 481
0.3%
ValueCountFrequency (%)
100 400
0.3%
99 434
0.3%
98 603
0.4%
97 403
0.3%
96 462
0.3%
95 465
0.3%
94 559
0.4%
93 607
0.4%
92 648
0.4%
91 617
0.4%

Pressure9am
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct546
Distinct (%)0.4%
Missing15065
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean1017.6499
Minimum980.5
Maximum1041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-05-02T23:12:27.562915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum980.5
5-th percentile1006.2
Q11012.9
median1017.6
Q31022.4
95-th percentile1029.5
Maximum1041
Range60.5
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation7.1065303
Coefficient of variation (CV)0.0069832759
Kurtosis0.23156262
Mean1017.6499
Median Absolute Deviation (MAD)4.7
Skewness-0.095523637
Sum1.3269646 × 108
Variance50.502773
MonotonicityNot monotonic
2023-05-02T23:12:27.659688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1016.4 816
 
0.6%
1017.9 789
 
0.5%
1016.3 775
 
0.5%
1018.7 775
 
0.5%
1018 769
 
0.5%
1017.3 769
 
0.5%
1015.9 768
 
0.5%
1017.8 766
 
0.5%
1017.2 759
 
0.5%
1017.7 759
 
0.5%
Other values (536) 122650
84.3%
(Missing) 15065
 
10.4%
ValueCountFrequency (%)
980.5 1
< 0.1%
982 1
< 0.1%
982.2 1
< 0.1%
982.3 1
< 0.1%
982.9 2
< 0.1%
983.7 1
< 0.1%
983.9 1
< 0.1%
984.4 1
< 0.1%
984.6 2
< 0.1%
985 1
< 0.1%
ValueCountFrequency (%)
1041 1
 
< 0.1%
1040.9 1
 
< 0.1%
1040.6 2
< 0.1%
1040.5 1
 
< 0.1%
1040.4 3
< 0.1%
1040.3 3
< 0.1%
1040.2 2
< 0.1%
1040.1 3
< 0.1%
1040 1
 
< 0.1%
1039.9 3
< 0.1%

Pressure3pm
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct549
Distinct (%)0.4%
Missing15028
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean1015.2559
Minimum977.1
Maximum1039.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-05-02T23:12:27.763120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum977.1
5-th percentile1004
Q11010.4
median1015.2
Q31020
95-th percentile1026.9
Maximum1039.6
Range62.5
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation7.0374138
Coefficient of variation (CV)0.0069316651
Kurtosis0.12917156
Mean1015.2559
Median Absolute Deviation (MAD)4.8
Skewness-0.045621405
Sum1.3242186 × 108
Variance49.525193
MonotonicityNot monotonic
2023-05-02T23:12:27.859350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1015.3 786
 
0.5%
1015.5 783
 
0.5%
1015.6 776
 
0.5%
1015.7 773
 
0.5%
1013.5 767
 
0.5%
1015.1 766
 
0.5%
1015.8 765
 
0.5%
1015.4 756
 
0.5%
1016 747
 
0.5%
1014.8 745
 
0.5%
Other values (539) 122768
84.4%
(Missing) 15028
 
10.3%
ValueCountFrequency (%)
977.1 1
< 0.1%
978.2 1
< 0.1%
979 1
< 0.1%
980.2 2
< 0.1%
981.2 1
< 0.1%
981.4 1
< 0.1%
981.9 1
< 0.1%
982.2 1
< 0.1%
982.6 1
< 0.1%
982.9 1
< 0.1%
ValueCountFrequency (%)
1039.6 1
 
< 0.1%
1038.9 1
 
< 0.1%
1038.5 1
 
< 0.1%
1038.4 1
 
< 0.1%
1038.2 1
 
< 0.1%
1038 1
 
< 0.1%
1037.9 2
< 0.1%
1037.8 2
< 0.1%
1037.7 3
< 0.1%
1037.6 1
 
< 0.1%

Cloud9am
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing55888
Missing (%)38.4%
Infinite0
Infinite (%)0.0%
Mean4.4474613
Minimum0
Maximum9
Zeros8642
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-05-02T23:12:27.944179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.8871589
Coefficient of variation (CV)0.6491701
Kurtosis-1.5388305
Mean4.4474613
Median Absolute Deviation (MAD)3
Skewness-0.22908183
Sum398368
Variance8.3356862
MonotonicityNot monotonic
2023-05-02T23:12:28.009139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 19972
 
13.7%
1 15687
 
10.8%
8 14697
 
10.1%
0 8642
 
5.9%
6 8171
 
5.6%
2 6500
 
4.5%
3 5914
 
4.1%
5 5567
 
3.8%
4 4420
 
3.0%
9 2
 
< 0.1%
(Missing) 55888
38.4%
ValueCountFrequency (%)
0 8642
5.9%
1 15687
10.8%
2 6500
 
4.5%
3 5914
 
4.1%
4 4420
 
3.0%
5 5567
 
3.8%
6 8171
5.6%
7 19972
13.7%
8 14697
10.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
8 14697
10.1%
7 19972
13.7%
6 8171
5.6%
5 5567
 
3.8%
4 4420
 
3.0%
3 5914
 
4.1%
2 6500
 
4.5%
1 15687
10.8%
0 8642
5.9%

Cloud3pm
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing59358
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean4.5099301
Minimum0
Maximum9
Zeros4974
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-05-02T23:12:28.079478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7203573
Coefficient of variation (CV)0.60319279
Kurtosis-1.4565245
Mean4.5099301
Median Absolute Deviation (MAD)2
Skewness-0.22638435
Sum388314
Variance7.4003439
MonotonicityNot monotonic
2023-05-02T23:12:28.143857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 18229
 
12.5%
1 14976
 
10.3%
8 12660
 
8.7%
6 8978
 
6.2%
2 7226
 
5.0%
3 6921
 
4.8%
5 6815
 
4.7%
4 5322
 
3.7%
0 4974
 
3.4%
9 1
 
< 0.1%
(Missing) 59358
40.8%
ValueCountFrequency (%)
0 4974
 
3.4%
1 14976
10.3%
2 7226
 
5.0%
3 6921
 
4.8%
4 5322
 
3.7%
5 6815
 
4.7%
6 8978
6.2%
7 18229
12.5%
8 12660
8.7%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 12660
8.7%
7 18229
12.5%
6 8978
6.2%
5 6815
 
4.7%
4 5322
 
3.7%
3 6921
 
4.8%
2 7226
 
5.0%
1 14976
10.3%
0 4974
 
3.4%

Temp9am
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct441
Distinct (%)0.3%
Missing1767
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean16.990631
Minimum-7.2
Maximum40.2
Zeros36
Zeros (%)< 0.1%
Negative443
Negative (%)0.3%
Memory size1.1 MiB
2023-05-02T23:12:28.228529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-7.2
5-th percentile6.9
Q112.3
median16.7
Q321.6
95-th percentile28.2
Maximum40.2
Range47.4
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation6.4887531
Coefficient of variation (CV)0.38190182
Kurtosis-0.34052334
Mean16.990631
Median Absolute Deviation (MAD)4.6
Skewness0.088539997
Sum2441434.8
Variance42.103917
MonotonicityNot monotonic
2023-05-02T23:12:28.328697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 912
 
0.6%
13.8 900
 
0.6%
14.8 894
 
0.6%
16 882
 
0.6%
14 876
 
0.6%
15 867
 
0.6%
16.6 867
 
0.6%
16.5 856
 
0.6%
13 848
 
0.6%
15.1 846
 
0.6%
Other values (431) 134945
92.8%
(Missing) 1767
 
1.2%
ValueCountFrequency (%)
-7.2 1
 
< 0.1%
-7 1
 
< 0.1%
-6.2 1
 
< 0.1%
-5.9 1
 
< 0.1%
-5.6 2
 
< 0.1%
-5.5 2
 
< 0.1%
-5.3 2
 
< 0.1%
-5.2 5
< 0.1%
-4.9 1
 
< 0.1%
-4.8 2
 
< 0.1%
ValueCountFrequency (%)
40.2 1
< 0.1%
39.4 1
< 0.1%
39.1 1
< 0.1%
39 1
< 0.1%
38.9 1
< 0.1%
38.6 1
< 0.1%
38.3 1
< 0.1%
38.2 1
< 0.1%
38 1
< 0.1%
37.9 1
< 0.1%

Temp3pm
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct502
Distinct (%)0.4%
Missing3609
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean21.68339
Minimum-5.4
Maximum46.7
Zeros17
Zeros (%)< 0.1%
Negative180
Negative (%)0.1%
Memory size1.1 MiB
2023-05-02T23:12:28.428533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-5.4
5-th percentile11.6
Q116.6
median21.1
Q326.4
95-th percentile33.7
Maximum46.7
Range52.1
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation6.9366505
Coefficient of variation (CV)0.31990618
Kurtosis-0.13628147
Mean21.68339
Median Absolute Deviation (MAD)4.8
Skewness0.23796036
Sum3075810.6
Variance48.11712
MonotonicityNot monotonic
2023-05-02T23:12:28.525495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 882
 
0.6%
19 869
 
0.6%
18.5 869
 
0.6%
18.4 868
 
0.6%
17.8 859
 
0.6%
19.4 840
 
0.6%
18 839
 
0.6%
19.2 839
 
0.6%
17 834
 
0.6%
19.3 833
 
0.6%
Other values (492) 133319
91.7%
(Missing) 3609
 
2.5%
ValueCountFrequency (%)
-5.4 1
 
< 0.1%
-5.1 1
 
< 0.1%
-4.4 1
 
< 0.1%
-4.2 1
 
< 0.1%
-4.1 1
 
< 0.1%
-4 1
 
< 0.1%
-3.9 2
< 0.1%
-3.8 1
 
< 0.1%
-3.7 3
< 0.1%
-3.5 3
< 0.1%
ValueCountFrequency (%)
46.7 1
 
< 0.1%
46.2 1
 
< 0.1%
46.1 3
< 0.1%
45.9 1
 
< 0.1%
45.8 2
< 0.1%
45.4 1
 
< 0.1%
45.3 2
< 0.1%
45.2 2
< 0.1%
45 1
 
< 0.1%
44.9 1
 
< 0.1%

RainToday
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing3261
Missing (%)2.2%
Memory size284.2 KiB
False
110319 
True
31880 
(Missing)
 
3261
ValueCountFrequency (%)
False 110319
75.8%
True 31880
 
21.9%
(Missing) 3261
 
2.2%
2023-05-02T23:12:28.623942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

RainTomorrow
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing3267
Missing (%)2.2%
Memory size284.2 KiB
False
110316 
True
31877 
(Missing)
 
3267
ValueCountFrequency (%)
False 110316
75.8%
True 31877
 
21.9%
(Missing) 3267
 
2.2%
2023-05-02T23:12:28.695727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Interactions

2023-05-02T23:12:20.899018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:54.684338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:56.333563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:58.117463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:59.884169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:01.664734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:03.304671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:05.294924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:07.095364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:08.886130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:10.693449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:12.644255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:14.278036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:15.907923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:17.473176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:19.238527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:21.022356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:54.798812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:56.562056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:58.227266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:59.981362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:01.776615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:03.417231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:05.415557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:07.198663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:09.002662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:10.797801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:12.746407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:14.378153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:16.004757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:17.570124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:19.335891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:21.143982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:54.912570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:56.663167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:58.351311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:00.076305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:01.883407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:03.525131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:05.540063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:07.302883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:09.120400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:10.901713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:12.847662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:14.478862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:16.094045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:17.660684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:19.434267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:21.270112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:55.013457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:56.766781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:58.463294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:00.185903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:01.988725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:03.631065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:05.645118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:07.403877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:09.225924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:11.003958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:12.949409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:14.579566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:16.202454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:17.754912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:19.533257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:21.379344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:55.110973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:56.863951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:58.569666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:00.475755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:02.095981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:03.725402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:05.742758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:07.520877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:09.344047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:11.371034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:13.050908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:14.680076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:16.310711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:17.849144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:19.635148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:21.488307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:55.215148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:56.968600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:58.686586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:00.585676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:02.196979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:03.835892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:05.871030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:07.658797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:09.464601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:11.486762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:13.159311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:14.788002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:16.429572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:17.945147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:19.741412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:21.597261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:55.321148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:57.075503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:58.800232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:00.685480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:02.299710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:03.948902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:05.992632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:07.794064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:09.575213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:11.599533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:13.267847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:14.898648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:16.547010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:18.311876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:19.849940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:21.699452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:55.425601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:57.180886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:58.908282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:00.787799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:02.400963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:04.053608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:06.106094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:07.906352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:09.697441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:11.700765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:13.369587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:15.002872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:16.648370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:18.402507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:19.955374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:21.800478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:55.523699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:57.297235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:59.008881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:00.893121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:02.502179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:04.170851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:06.206624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:08.009254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:09.817262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:11.804929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:13.469811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:15.106143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:16.738694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:18.493503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:20.057249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:21.904671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:55.624270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:57.409187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:59.135803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:00.988158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:02.597388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:04.288519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:06.317273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:08.122087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:09.936591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:11.917964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:13.572995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:15.208985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:16.830353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:18.585880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:20.160507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:22.005913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:55.725271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:57.516546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:59.250968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:01.083247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:02.703509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:04.406451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:06.435687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:08.240408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:10.052336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:12.031319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:13.677076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:15.312189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:16.923337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:18.678021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:20.269240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:22.114552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:55.832851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:57.624606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:59.368053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:01.184369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:02.816045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:04.522981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:06.546806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:08.366661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:10.170476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:12.148205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:13.783365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:15.417941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:17.019420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:18.774493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:20.380972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:22.212610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:55.931399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:57.722676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:59.475964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:01.283891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-05-02T23:12:06.649884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:08.467669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-05-02T23:12:13.883161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:15.520315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:17.115914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:18.869077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:20.478009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:22.303930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:56.029213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:57.815169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:59.574866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:01.374877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:03.014805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:04.722693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:06.751013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:08.568098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:10.365741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:12.344698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:13.976979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:15.613539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:17.204334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:18.958177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:20.568782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:22.405919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:56.134495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:57.917964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:59.677804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:01.468971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:03.106919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:05.072917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:06.875987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:08.683390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:10.470354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:12.445532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:14.076823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:15.714653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:17.293794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:19.050353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:20.668294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:22.505575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:56.234454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:58.019384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:11:59.784096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:01.573440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:03.200037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:05.186552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:06.991721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:08.784440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:10.589101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:12.545818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:14.176296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:15.814400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:17.383177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:19.140292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-02T23:12:20.765777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-05-02T23:12:28.777465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2023-05-02T23:12:28.935380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-05-02T23:12:29.073143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-05-02T23:12:29.210533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-05-02T23:12:29.338299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-05-02T23:12:29.440197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-05-02T23:12:22.731196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-02T23:12:23.183099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-02T23:12:24.114451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
02008-12-01Albury13.422.90.6NaNNaNW44.0WWNW20.024.071.022.01007.71007.18.0NaN16.921.8NoNo
12008-12-02Albury7.425.10.0NaNNaNWNW44.0NNWWSW4.022.044.025.01010.61007.8NaNNaN17.224.3NoNo
22008-12-03Albury12.925.70.0NaNNaNWSW46.0WWSW19.026.038.030.01007.61008.7NaN2.021.023.2NoNo
32008-12-04Albury9.228.00.0NaNNaNNE24.0SEE11.09.045.016.01017.61012.8NaNNaN18.126.5NoNo
42008-12-05Albury17.532.31.0NaNNaNW41.0ENENW7.020.082.033.01010.81006.07.08.017.829.7NoNo
52008-12-06Albury14.629.70.2NaNNaNWNW56.0WW19.024.055.023.01009.21005.4NaNNaN20.628.9NoNo
62008-12-07Albury14.325.00.0NaNNaNW50.0SWW20.024.049.019.01009.61008.21.0NaN18.124.6NoNo
72008-12-08Albury7.726.70.0NaNNaNW35.0SSEW6.017.048.019.01013.41010.1NaNNaN16.325.5NoNo
82008-12-09Albury9.731.90.0NaNNaNNNW80.0SENW7.028.042.09.01008.91003.6NaNNaN18.330.2NoYes
92008-12-10Albury13.130.11.4NaNNaNW28.0SSSE15.011.058.027.01007.01005.7NaNNaN20.128.2YesNo
DateLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
1454502017-06-16Uluru5.224.30.0NaNNaNE24.0SEE11.011.053.024.01023.81020.0NaNNaN12.323.3NoNo
1454512017-06-17Uluru6.423.40.0NaNNaNESE31.0SESE15.017.053.025.01025.81023.0NaNNaN11.223.1NoNo
1454522017-06-18Uluru8.020.70.0NaNNaNESE41.0SEE19.026.056.032.01028.11024.3NaN7.011.620.0NoNo
1454532017-06-19Uluru7.420.60.0NaNNaNE35.0ESEE15.017.063.033.01027.21023.3NaNNaN11.020.3NoNo
1454542017-06-20Uluru3.521.80.0NaNNaNE31.0ESEE15.013.059.027.01024.71021.2NaNNaN9.420.9NoNo
1454552017-06-21Uluru2.823.40.0NaNNaNE31.0SEENE13.011.051.024.01024.61020.3NaNNaN10.122.4NoNo
1454562017-06-22Uluru3.625.30.0NaNNaNNNW22.0SEN13.09.056.021.01023.51019.1NaNNaN10.924.5NoNo
1454572017-06-23Uluru5.426.90.0NaNNaNN37.0SEWNW9.09.053.024.01021.01016.8NaNNaN12.526.1NoNo
1454582017-06-24Uluru7.827.00.0NaNNaNSE28.0SSEN13.07.051.024.01019.41016.53.02.015.126.0NoNo
1454592017-06-25Uluru14.9NaN0.0NaNNaNNaNNaNESEESE17.017.062.036.01020.21017.98.08.015.020.9NoNaN